ML Model Code Integration in IDEs

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Solution Overview

Problem

Software developers face barriers when integrating machine learning models into software development environments due to the need for additional configuration and the requirement to adopt different approaches from object-oriented programming, which can be unfamiliar and require significant setup of separate development environments.

Innovation Solution

Integrating machine learning models into integrated development environments (IDEs) by representing them as first-class objects, using a standard specification for description, and generating code interfaces that leverage IDE features like syntax checking and auto-completion, allowing developers to work with ML models seamlessly within the familiar object-oriented programming paradigm.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Adaptability or versatility

If machine learning models are integrated into IDEs with full configuration and separate development environments, then the functionality and power of ML models are improved, but the complexity and difficulty of use increase significantly

Engineering Contradiction:

ImproveML model functionalityVSAvoiddevelopment environment complexity

Core Design Contradiction:

Adaptability or versatilityVSDevice complexity

Solution Approach 1:

The patent merges ML model development capabilities directly into the IDE environment, combining previously separate ML development tools and workflows with the familiar IDE interface. This integration allows developers to work with ML models using the same environment they use for traditional software development, reducing the need for separate configuration and development setups while maintaining full ML functionality.

Inventive Principle:

Principle #5Merging (Combining)

Solution Approach 2:

The IDE is enhanced to serve multiple functions - it now handles both traditional software development tasks and ML model development tasks within a single unified environment. The system provides universal support for different programming languages and ML frameworks through the same IDE interface, eliminating the need for specialized separate environments for different ML workflows.

Inventive Principle:

Principle #6Universality (Multi-functionality)

2Adaptability or versatility

If machine learning models are integrated into IDEs with full configuration requirements, then the functionality and power of ML models are improved, but the ease of operation decreases due to unfamiliar approaches

Engineering Contradiction:

ImproveML model functionalityVSAvoiddeveloper usability

Core Design Contradiction:

Adaptability or versatilityVSEase of operation

Solution Approach 1:

The system adapts the IDE interface and tools to match the specific requirements of ML model development at different stages. Instead of requiring developers to learn entirely new approaches, the IDE provides localized adjustments and specialized views that appear contextually relevant to the current ML task while maintaining consistency with the overall familiar IDE paradigm.

Inventive Principle:

Principle #3Local quality

Solution Approach 2:

Rather than requiring developers to adapt to new ML-specific development environments and approaches, the patent inverts the approach by bringing ML capabilities into the existing familiar IDE environment. This allows developers to work with ML models using their existing knowledge and workflows, eliminating the need to learn unfamiliar approaches while still providing full ML functionality.

Inventive Principle:

Principle #13The other way round (Inversion)

3Adaptability or versatility

If machine learning models are used with separate development environments, then the specialized ML capabilities are improved, but the productivity and integration with existing workflows decrease

Engineering Contradiction:

ImproveML model capabilitiesVSAvoidsoftware development productivity

Core Design Contradiction:

Adaptability or versatilityVSProductivity

Solution Approach 1:

The patent combines ML model development capabilities with the existing IDE and software development workflow into a single integrated environment. This merging eliminates the need to switch between separate ML development tools and traditional IDEs, allowing developers to seamlessly integrate ML model creation, training, and deployment within their existing software development processes, thereby maintaining high productivity while providing specialized ML capabilities.

Inventive Principle:

Principle #5Merging (Combining)

Data Source

PatentUS10606566B2Integration of learning models into a software development system

Publication Date: 2020.03.31 APPLE INC

AI summary

The subject technology provides for generating machine learning (ML) model code from a ML document file, the ML document file being in a first data format, the ML document file being converted to code in an object oriented programming language different than the first data format. The subject technology further provides for receiving additional code that calls a function provided by the ML model code. The subject technology compiles the ML model code and the additional code, the compiled ML model code including object code corresponding to the compiled ML model code and the compiled additional code including object code corresponding to the additional code. The subject technology generates a package including the compiled ML model code and the compiled additional code. Further, the subject technology sends the package to a runtime environment on a target device for execution.